The present invention relates to wireless full-duplex communications and more particularly to interference cancellation for full-duplex communications.
We consider the problem of canceling self-interference received by a wireless node from the transmitted signal from the same node. The self-interference signal is partly known to the transmitter with the exception of the channel gain and multipath effect. We investigate the limitation of digital cancellation in solving the problem of self-interference cancellation and seek efficient algorithms that can be used in wideband and frequency selective channels as well as the narrowband and frequency flat channels. We show that limitations of analog to digital converters (ADC) such as the dynamic range and quantization resolution are the main obstacle in restricting the isolation levels of the self-interference signal that can be achieved by employing digital cancelation. We provide design guidelines and a specific digital cancelation system with an enhanced effective resolution and larger dynamic range by feeding back the canceling signal prior to analog to digital conversion. We also address the problem of wideband digital cancellation by using the theory of sparse signal recovery.
The transmission and reception in the current commercial wireless communication systems occurs in orthogonal resource blocks (RB) where a resource block indicates a particular division of space, frequency, and time. In particular, the cellular systems are designed such that uplink and downlink transmissions work in orthogonal time, i.e., time division duplex (TDD) systems, or frequency division duplex (FDD) systems. Due to the orthogonality constraint between the resource blocks for transmission and reception from any network terminals, e.g., the base stations or relays, the system works in so called half duplex (HD) mode. A full-duplex wireless device is one that can transmit and receive at the same time in the same frequency band and typically requires at least one Tx and one Rx antenna. The key challenge in realizing such a device lies in the Self-Interference (SI) generated by the Tx antenna at the Rx antenna. As an example, consider a WiFi signal with a transmit power of 20 dBm. A Tx-Rx antenna separation of about 6-8 inches results in a path loss of about 40 dB (depending on channel characteristics), resulting in a self-interference of at least −20 dBm. With a noise floor around −93 dBm, one would further require a self-interference cancellation of at least 73 dB to be able to decode the desired received signal. While one can solely employ digital interference cancellation techniques, current analog to digital converters (ADC's) do not have a resolution to pass a received signal which is 73 dB less than the noise floor. Hence, several practical full duplex (FD) systems [1, 2, 3] have been proposed that couple RF cancellation along with digital cancellation to achieve the desired level of SI suppression.
Digital noise cancellation has been vastly addressed in the literature in the context of active noise cancellation where it is particularly designed for reducing the acoustic noise by generating interfering signals that enables cancellation of an unwanted signal at a particular location. In spite of the similarity of active noise cancellation and self-interference cancellation for the purpose of wireless full duplex communication there are subtle differences between the two problems:
Firstly, in the problem of active noise cancellation, the unwanted signal is not known and only a statistical model of the signal is assumed to be known. An example of such signals is the noise generated by the propeller of an airplane or helicopter, the noise generated by the engine of the airplane or a car in steady motion, etc. The road noise in an automobile generated by the tires is also another example of such signal.
The second difference is that the signal has to be generated and then propagated in the air to reach the desired location where the null is intended. Therefore, due to the considerable delay in propagation of acoustic signals over the distance, it is required that the future value of a signal be predicted rather than the current value of the signal. In contrast, in self-interference cancellation the interfering signal is known and given the fast propagation of the electromagnetic signals, prediction of its future value is not an issue in this context. However, the power of self interference is comparable or much higher than (say 10 dB to 40 dB) that of the intended signal. Moreover, the intended signal has the same carrier frequency and therefore it is not practical to null a particular frequency. For example, consider a narrow band system with 10 kHz bandwidth at a center frequency of 2.4 GHz. At the receiver, the self-interference signal could be a modulated cosine wave with phase difference Φ1 and amplitude A1 while the intended signal is different only in phase and amplitude, say Φ2 and A2, respectively. Therefore by considering only a narrowband signal it is impossible to figure out the self-interference component from the received signal unless if the phase and amplitude of the self-interference is available a priori. However, for a wideband system the situation is different and the signature of the self-interference signal over different tones may be used for resolving this issue.
RF cancellation can include a combination of antenna cancellation and analog cancellation. In [1], antenna cancellation was achieved by placing two Tx antennas asymmetrically at l and l+(λ/2) distance from the Rx antenna, thereby allowing the transmit signals to add π out of phase and hence cancel each other. On the other hand, analog cancellation involves generation of the π phase shift internally, coupled with the estimation and compensation of the SI channel [2, 3]. This allows for it phase shifters with a better frequency response over a wide-band channel (e.g., BALUN in [3]) to be employed, in contrast to the strong dependence on frequency (λ) posed by the antenna cancellation in [1]. While the existing schemes employ at least two antennas, one can also envision FD with a single antenna [5], where a circulator is used to isolate the Tx and Rx signals. However, owing to the lack of path loss attenuation and the lack of contribution from RF cancellation, the required level of SI cancellation is significantly higher and hence hard to realize.
Current work on self-interference cancellation have considered four types of cancellations (i) Active noise cancellation in the air for example by using transmit antenna cancellation [1], (ii) Active noise cancellation in RF circuits and transmission lines for example by using receive antenna cancellation [4], (iii) Passive noise cancellation with analog noise canceler circuits [5], and (iv) Passive noise cancellation with digital noise canceler algorithms [2].
[1] J. Choi, M. Jain, K. Srinivasan, P. Levis, and S. Katti. Achieving single channel, full duplex wireless communication. In Proceedings of ACM MobiCom, September 2010.
[2] M. Duarte, C. Dick, and A. Sabharwal. Experiment-driven characterization of full-duplex wireless systems. Avaiable at: http://warp.rice.edu/trac/wiki/TransWireless2011_FullDuplex.
[3] M. Jain, T. M. Kim, D. Bharadia, S. Seth, K. Srinivasan, P. Levis, S. Katti, and P. Sinha. Practical, real-time, full duplex wireless. In Proceedings of ACM MobiCom, September 2011.
[4] M. A. Khojastepour, K. Sundaresan, S. Rangarajan, X. Zhang, and S. Barghi. The case for antenna cancellation for scalable full duplex wireless communications. Tenth ACM Workshop on Hot Topics in Networks (HotNets-X) November 14-15, Cambridge, Mass.
[5] M. Knox. Self-jamming Cancellation Networks for Full Duplex Communication. PHD Thesis, Polytechnic University, 2008.
We address the problem of digital cancelation of self-interference signal. While digital cancellation alone cannot provide the required isolation for the self-interference between the transmit to the receive RF chains, it is essential component and plays a unique role in enabling full-duplex systems. The problem with multipath and wideband signals is very hard to address in analog domain mainly due to the fact that wideband RF components are very hard to design, and design of multi stage cancelation circuits to cancel out the effect of different signal paths would be very expensive to build and hard to adjust. However, in digital domain, the main requirement is the resolution and dynamic range of ADC; processing in digital domain in order to generate wideband canceling signal is much simpler, less expensive, and efficient. We apply the theory of compressed sensing in order to find the solution to the problem of sparse channel impulse recovery for the purpose of self-interference cancelation.
We provide design guidelines as how to adjust or choose ADC parameters in order to meet particular SINR in the digital domain. We also provide efficient designs and algorithms that can be used in wideband and frequency selective channels as well as the narrowband and frequency flat channels. We show that limitations of analog to digital converters (ADC) such as the dynamic range and quantization resolution are the main obstacle in restricting the isolation levels of the self-interference signal that can be achieved by employing digital cancelation. We provide particular cancellation scenarios and system block diagrams with an enhanced effective resolution and larger dynamic range by feeding back the canceling signal prior to analog to digital conversion. We also provide new scheme for solving the problem of wideband digital cancellation by employing ideas from the theory of sparse signal recovery.
The “DETAILED DESCRIPTION” is organized as follows. In Section 1, we discuss the limits of digital noise cancellation due to the performance limitation of ADC. In Section 2, we discuss the problem of self-interference cancellation and the approach we take to solve this problem. In Section 3, we explain the channel estimation problem for the purpose of digital noise cancellation. We describe the relation between the channel estimation and sparse signal recovery problems in Section 4. We provide specific algorithms for channel estimation based on the compressed sensing algorithm in Section 5. We discuss the generation of the self-cancelation signals under different scenarios in Section 6. We provide further simulation results in Section 7. Finally, conclusion is described in Section 8.